improvement

Calculates the improvement of each age, based on the resulting chains of the beta parameter from a fitted blc model.

Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>.

Luiz Figueiredo

BayesMortalityPlus

Bayesian Mortality Modelling

improvement function

<dl><dt>obj</dt>
<dd>A <code>BLC</code> object, result of a call to blc() function.</dd>
<dt>prob</dt>
<dd>A real number that represents the credibility level of the intervals.</dd></dl>

Arguments

BLC: Improvement — improvement

<dl>

<dt>obj</dt>
<dd>A <code>BLC</code> object, result of a call to blc() function.</dd>


<dt>prob</dt>
<dd>A real number that represents the credibility level of the intervals.</dd>

</dl>

improvement: BLC: Improvement

Description

Usage

Value

Arguments

Examples